Electrocardiogram (ECG) data from patients with a variety of heart conditions are studied using ordinal pattern partition networks. The ordinal pattern partition networks are formed from the ECG time series by symbolizing the data into ordinal patterns. The ordinal patterns form the nodes of the network and edges are defined through the time ordering of the ordinal patterns in the symbolized time series. A network measure, called the mean degree, is computed from each time series-generated network. In addition, the entropy and number of non-occurring ordinal patterns (NFP) is computed for each series. The distribution of mean degrees, entropies, and NFPs for each heart condition studied is compared. A statistically significant difference between healthy patients and several groups of unhealthy patients with varying heart conditions is found for the distributions of the mean degrees, unlike for any of the distributions of the entropies or NFPs.

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In forensics DNA typing is usually based on STRs, but sometimes challenging samples demand the development of optimized DNA extraction methodologies. In addition, when DNA is severely degraded or in small amounts, results can be inconclusive. Thus another approaches, as miniSTRs, which consist on STRs redesigned to be more sensitive and generate smaller amplicons, are recommended. This study aimed to test an optimized, less time-expensive DNA extraction methodology for human bones combined with STRs and Mini-STRs on samples with previous inconclusive results. Our findings showed an efficiency improvement on both typing methodologies, which suggests that the optimized extraction method is promising.